The Role of Psychological Determinants and Demographic Factors in Consumer Demand For Farm-To-Fork Traceability Systems
Bibliographic record
Abstract
Traceability systems are an important tool (1) for tracking, monitoring, and managing product flows through the supply chain for better efficiency and profitability of suppliers, and (2) to improve consumer confidence in the face of serious food safety incidents. After the global bovine spongiform encephalopathy (BSE) crisis affected producers, consumers, trade, and the health status of animals and humans, new systems to help confirm the status of cattle products along the supply chain from farm to fork were implemented in many countries (Trautman et al. 2008 Trautman, D., Goddard, E., Nilsson, T. 2008. Traceability—A literature review. Project report 08-02. Department of Rural Economy, University of Alberta, Edmonton, Canada. http://https://re.sitecore.ualberta.ca/en/Research/~/media/University%20of%20Alberta/Faculties/ALES/Departments/Rural%20Economy/Department%20Site/Documents/Project%20Reports/projectreport0802.ashx (http://https://re.sitecore.ualberta.ca/en/Research/~/media/University%20of%20Alberta/Faculties/ALES/Departments/Rural%20Economy/Department%20Site/Documents/Project%20Reports/projectreport0802.ashx) [Google Scholar]). In this study, people's overall food safety beliefs are explored with the main objective of measuring the link between their food safety beliefs and their attitudes toward traceability. A comparison is made among English-speaking Canadians, French-speaking Canadians, and Japanese consumers. In the study, an Internet-based survey was used to collect data from nationally representative samples of the population in Canada-English (1275), Canada-French (343), and Japanese (1940) in the summer of 2009. Respondents' interests in traceability systems are clearly linked to their sense that the industry is primarily responsible for any food safety outbreaks. Moreover, it is clear that certain segments of the population in all samples feel strongly about the importance of farm to fork traceability in beef; thus, policymakers may wish to consider extending traceability beyond the point of slaughter as a way of encouraging beef sales in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".